Paper: arXiv 2405.15833

Abstract

In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation. Traditional methods transform raw stock data of varying frequencies into predictive characteristic factors for asset sorting, often requiring extensive manual design and misalignment between prediction and optimization goals. To address these challenges, we introduce Direct Sorted Portfolio Optimization (DSPO), an innovative end-to-end framework that efficiently processes raw stock data to construct sorted portfolios directly. DSPO’s neural network architecture seamlessly transitions stock data from input to output while effectively modeling the intra-dependency of time-steps and inter-dependency among all tradable stocks. Additionally, we incorporate a novel Monotonical Logistic Regression loss, which directly maximizes the likelihood of constructing optimal sorted portfolios. To the best of our knowledge, DSPO is the first method capable of handling market cross-sections with thousands of tradable stocks fully end-to-end from raw multi-frequency data. Empirical results demonstrate DSPO’s effectiveness, yielding a RankIC of 10.12% and an accumulated return of 121.94% on the New York Stock Exchange in 2023-2024, and a RankIC of 9.11% with a return of 108.74% in other markets during 2021-2022.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced neural network architectures (Transformers, CNNs) and a novel Monotonical Logistic Regression loss, indicating high mathematical sophistication. The empirical evaluation is strong, featuring backtest results with specific metrics (RankIC, accumulated returns) across major markets (NYSE, A-Share) and ablation studies, demonstrating significant implementation and data validation.

Research Flowchart

  flowchart TD
  Start["Research Goal<br>Construct sorted portfolios directly from raw stock data"] --> Input["Raw Multi-Frequency Data<br>NYSE (2023-2024) & Other Markets (2021-2022)"]
  
  Input --> Method["Methodology: DSPO Framework<br>End-to-End Neural Network Architecture"]
  
  Method --> Process["Computational Process<br>1. Intra-dependency (Time-steps)<br>2. Inter-dependency (Cross-section)<br>3. Monotonical Logistic Regression Loss"]
  
  Process --> Outcome["Key Findings & Outcomes<br>RankIC: 10.12% | Return: 121.94% (NYSE)<br>RankIC: 9.11% | Return: 108.74% (Others)"]
  
  style Start fill:#f9f,stroke:#333,stroke-width:2px
  style Input fill:#ccf,stroke:#333,stroke-width:2px
  style Method fill:#cfc,stroke:#333,stroke-width:2px
  style Process fill:#cff,stroke:#333,stroke-width:2px
  style Outcome fill:#ffc,stroke:#333,stroke-width:2px